Haas, M., Krause, J., Paolella, M.S. and Steude, S.C. (2013) Time-Varying Mixture GARCH Models and Asymmetric Volatility. North American Journal of Economics and Finance, 26, 602-623.
https://doi.org/10.1016/j.najef.2013.02.024

has been cited by the following article:

• TITLE: Variable Selection in Finite Mixture of Time-Varying Regression Models

  AUTHORS: Jing Liu, Wanzhou Ye

  KEYWORDS: Mixture Regression Models, GARCH, Block-Wise MM algorithm, LASSO, SCAD

  JOURNAL NAME: Advances in Pure Mathematics, Vol.10 No.3, March 6, 2020

  ABSTRACT: In this paper, we research the regression problem of time series data from heterogeneous populations on the basis of the finite mixture regression model. We propose two finite mixed time-varying regression models to solve this. A regularization method for variable selection of the models is proposed, which is a mixture of the appropriate penalty functions and l2 penalty. A Block-wise minimization maximization (MM) algorithm is used for maximum penalized log quasi-likelihood estimation of these models. The procedure is illustrated by analyzing simulations and with an application to analyze the behavior of urban vehicular traffic of the city of São Paulo in the period from 14 to 18 December 2009, which shows that the proposed models outperform the FMR models.